1. Genetic Algorithms

2. Solution Search in Problem Space

4. The Genetic Algorithm  Directed search algorithms based on the mechanics of biological evolution  Developed by John Holland, University of Michigan (1970’s)  To understand the adaptive processes of natural systems  To design artificial systems software that retains the robustness of natural systems

5. The Genetic Algorithm (cont.)  Provide efficient, effective techniques for optimization and machine learning applications  Widely-used today in business, scientific and engineering circles

6. Classes of Search Techniques

7. Components of a GA A problem to solve, and...  Encoding technique (gene, chromosome)  Initialization procedure (creation)  Evaluation function (environment)  Selection of parents (reproduction)  Genetic operators (mutation, recombination)  Parameter settings (practice and art)

8. Simple Genetic Algorithm { initialize population; evaluate population; while TerminationCriteriaNotSatisfied { select parents for reproduction; perform recombination and mutation; evaluate population; }

9. The GA Cycle of Reproduction reproduction population evaluation modification discard deleted members parents children modified children evaluated children

10. Population Chromosomes could be:  Bit strings (0101... 1100)  Real numbers (43.2 -33.1... 0.0 89.2)  Permutations of element (E11 E3 E7... E1 E15)  Lists of rules (R1 R2 R3... R22 R23)  Program elements (genetic programming) ... any data structure... population

11. Reproduction reproduction population parents children Parents are selected at random with selection chances biased in relation to chromosome evaluations.

12. Evaluation and Selection evaluate fitness of each solution in current population (e.g., ability to classify/discriminate) [involves genotype-phenotype decoding] selection of individuals for survival based on probabilistic function of fitness may include elitist step to ensure survival of fittest individual on average mean fitness of individuals increases

13. Roulette Wheel Selection

14. Chromosome Modification  Modifications are stochastically triggered  Operator types are:  Mutation  Crossover (recombination) modification children modified children

15. Mutation: Local Modification  Causes movement in the search space (local or global)  Restores lost information to the population Before: (1 0 1 1 0 1 1 0) After: (0 1 1 0 0 1 1 0) Before: (1.38 -69.4 326.44 0.1) After: (1.38 -67.5 326.44 0.1)

16. Crossover: Recombination P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1 P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2 Crossover is a critical feature of genetic algorithms:  It greatly accelerates search early in evolution of a population  It leads to effective combination of schemata (subsolutions on different chromosomes) *

17. Crossover combine two individuals to create new individuals for possible inclusion in next generation main operator for local search (looking close to existing solutions) perform each crossover with probability p c {0.5,…,0.8} crossover points selected at random individuals not crossed carried over in population

19. Mutation each component of every individual is modified with probability p m main operator for global search (looking at new areas of the search space) individuals not mutated carried over in population p m usually small {0.001,…,0.01} rule of thumb = 1/no. of bits in chromosome

24. Evaluation  The evaluator decodes a chromosome and assigns it a fitness measure  The evaluator is the only link between a classical GA and the problem it is solving evaluation evaluated children modified children

25. Deletion  Generational GA: entire populations replaced with each iteration  Steady-state GA: a few members replaced each generation population discard discarded members

26. An Abstract Example Distribution of Individuals in Generation 0 Distribution of Individuals in Generation N

27. A Simple Example The Traveling Salesman Problem: Find a tour of a given set of cities so that  each city is visited only once  the total distance traveled is minimized

28. TSP Problem

30. Representation Representation is an ordered list of city numbers known as an order-based GA. 1) London 3) Dunedin 5) Beijing 7) Tokyo 2) Venice 4) Singapore 6) Phoenix 8) Victoria CityList1 (3 5 7 2 1 6 4 8) CityList2 (2 5 7 6 8 1 3 4)

31. Crossover Crossover combines inversion and recombination: * * Parent1 (3 5 7 2 1 6 4 8) Parent2 (2 5 7 6 8 1 3 4) Child (2 5 7 2 1 6 3 4) This operator is called the Order1 crossover.

32. Mutation Mutation involves reordering of the list: * Before: (5 8 7 2 1 6 3 4) After: (5 8 6 2 1 7 3 4)

33. TSP Example: 30 Cities

34. Solution i (Distance = 941)

35. Solution j (Distance = 800)

36. Solution k (Distance = 652)

37. Best Solution (Distance = 420)

38. Overview of Performance

39. Issues for GA Practitioners  Choosing basic implementation issues:  representation  population size, mutation rate,...  selection, deletion policies  crossover, mutation operators  Termination Criteria  Performance, scalability  Solution is only as good as the evaluation function (often hardest part)

40. Benefits of Genetic Algorithms  Concept is easy to understand  Modular, separate from application  Supports multi-objective optimization  Good for “noisy” environments  Always an answer; answer gets better with time  Inherently parallel; easily distributed

41. Benefits of Genetic Algorithms (cont.)  Many ways to speed up and improve a GA-based application as knowledge about problem domain is gained  Easy to exploit previous or alternate solutions  Flexible building blocks for hybrid applications  Substantial history and range of use

42. Some GA Application Types